With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.

Environment perception is the premise for intelligent vehicles to drive safely and stably. Despite the rapid development of road detection technology based on visual images, it is still challenging to robustly identify road areas in visual images due to the influence of illumination changes and noise. In order to solve this problem, we introduce a new optimized lidar and camera sensor fusion method for road environment sensing of intelligent vehicles. In road boundary detection based on laser data, a median point filtering method of ordered pole cloud is proposed. A method of boundary search, boundary seed point growth and obstacle clustering is proposed to identify road boundary. In the lane line classification based on visual image, a lane line search classification method is proposed, which can effectively classify lane lines and extract single lane lines. On the basis of the optimization of sensors, several constraint conditions are proposed based on the fusion of the two data, and the location of missing lane lines is predicted by using the road information identified by lidar and image, and the lane lines are identified again. Finally, a large number of experiments are carried out on kitti-Road benchmark data set, and a test platform is built to verify the results of the identification method proposed in this paper in rainy day, cloudy day, night and other special scenarios. Experimental results show that this method is superior to existing methods.

Computer vision has four basic tasks: image classification,^{1 – 4} object detection,^{5 – 9} semantic segmentation,^{10 – 13} and instance segmentation.^14,15 These technologies form the basis for addressing many practical problems. The main purpose of image classification is to find a classification label from the classification label set and then to assign the label to the input image. The task of object detection is not only to assign classification labels to images but also to mark the locations of specific objects. Both image classification and object detection are the foundation for solving complex problems in computer vision, such as object tracking,^{16 – 19} image segmentation, and scene interpretation. The task of semantic segmentation refers to assigning a category label to each pixel in an image, so as to segment and detect specific categories and finely label the boundaries. While distinguishing each category, instance segmentation also provides different labels for individual instances in the same type of objects. It can be regarded as delivering the tasks of object detection and semantic segmentation at the same time, which usually need to deal with multiple overlapping objects and complex backgrounds. This is the most challenging problem of the four basic tasks of computer vision and has important application value in the fields of intelligent driving,^{20 – 22} remote sensing detection,^{23 – 26} and medical health.^{27 – 30} The four basic tasks of computer vision are shown in Fig. 1. With breakthroughs in computer vision technology, as well as progress in wireless sensor network^{31 – 33} and multiple communications,^{34 – 39} especially in cloud computing^{40 – 42} and other techniques to lower the hardware requirements, it is possible to solve these practical problems.^{43 – 47} Currently, neural network technology is widely deployed.^{48 – 51} Instance segmentation can provide richer information than other computer vision tasks can, but it is also a more difficult job. Although the development of deep learning has greatly promoted the progress of instance segmentation tasks over recent years, there are still few related reviews, without a comprehensive summary of its latest development and a deep discussion of its current difficulties. This study will make an in-depth discussion of instance segmentation on the basis of image segmentation.

Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter‐wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense‐based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

In this paper, a vehicle center of gravity (CG) height estimation method based on improved You Only Look Once algorithm and AdaBoost regression is proposed by relying on roadside sensors. The CG height of a vehicle plays an important role in the performance evaluation indexes of vehicle handling, ride comfort, and safety. Therefore, the accurate measurement of the CG height is helpful for improving vehicle control and ensuring driving safety. Different from traditional vehicle modeling, in this study, a data-driven roadside estimation method based on machine vision is utilized for the estimation of the vehicle CG height, thereby providing an economic and convenient concept. In terms of vehicle type identification, the improved YOLO algorithm is characterized by flexibility and speed while ensuring high accuracy. This ensures that it can be quickly deployed on roadside equipment and meet real-time requirements. Moreover, through the correlation analysis, parameters including the vehicle’s front track width, rear track width, roof height, and weight, which can be collected by roadside sensors, are selected as input parameters for the regression. An ensemble learning model based on AdaBoost regression realizes the final estimation of the CG height. The test results obtained on the NHTSA experimental data show that the average accuracy of vehicle type identification reaches 97.59% and the regression estimation accuracy of the vehicle CG height reaches 94.64%.

This study presents an intelligent distribution framework based on edge computing and proposes navigation and obstacle avoidance algorithms for connected logistics vehicles (CLVs) on the basis of Trimble BD982 positioning sensor and tentacle algorithm (TA). An edge computing framework for the distribution of CLVs is established, and the functions of three layers (cloud server, edge equipment, and terminal) are described in detail. The basic functions, hardware, and software systems of the CLV are designed and presented. Focusing on autonomous driving, a Global Positioning System (GPS) navigation algorithm and an obstacle avoidance control strategy on the layer of edge equipment are developed on the basis of the TA. Autonomous GPS navigation is realized by combining the entire road network with the local road network to avoid obstacles. The TA is improved to help the CLV for avoiding obstacles. Experiments show that the hardware system and designed algorithms of the CLV are effective. The tracking error on the straight-line track is within 3 cm, the change rate of longitudinal velocity is within 0.3 g/s, the change rate of tire side deflection angle is less than 1°/s, and the calculation time is shortened by 25% when the calculation time is 30 ms. These results indicate that the vehicle has good stability and performance during obstacle avoidance in real time, and the proposed algorithms are superior to traditional algorithms. The CLV can realize autonomous GPS navigation, with high navigation accuracy, reliable obstacle avoidance performance, and stable vehicle handling.